About

This page is about exploratory data anlysis on estimated resident population, household, and employment specifically in NSW region at SA2 level.

Estimated Resident Population

The raw ERP dataset has 20808 observations and 17 variables. And about 6 variables provide useful information: sex, age, sa2_code, region, time, and value.

(erp <- read_csv(
  "../../Raw Data/Data Files/ABS/ERP/ABS_ERP_ASGS2016_25042019132433480.csv"
))

## Parsed with column specification:
## cols(
##   MEASURE = col_character(),
##   Measure = col_character(),
##   SEX_ABS = col_double(),
##   Sex = col_character(),
##   AGE = col_character(),
##   Age = col_character(),
##   REGIONTYPE = col_character(),
##   `Geography Level` = col_character(),
##   ASGS_2016 = col_double(),
##   Region = col_character(),
##   FREQUENCY = col_character(),
##   Frequency = col_character(),
##   TIME = col_double(),
##   Time = col_double(),
##   Value = col_double(),
##   `Flag Codes` = col_logical(),
##   Flags = col_logical()
## )

Population by Age and Sex

As seen in Figure 1, both genders have a similar distribution with the age ranges 25-29 and 30-34 at the peak. The population starts to decline in ages larger than 50. Over 85-year olds, the population of women is about twice as large as that of men.

erp <- erp %>% 
  select(-c("MEASURE", "Measure", "SEX_ABS", "AGE", "FREQUENCY", "Frequency", 
            "TIME", "Flag Codes", "Flags", "REGIONTYPE", "Geography Level")) %>% 
  rename(sa2_code = ASGS_2016) %>% 
  clean_names()
erp$age <- factor(erp$age, levels = erp$age, labels = erp$age)

erp %>% 
  mutate(value = ifelse(sex == "Males", -1 * value, value)) %>% 
  ggplot(mapping = aes(x = age, y = value, fill = sex)) +
  geom_bar(stat = "identity", width = .85) +
  scale_y_continuous(labels = c('300k', '200k', '100k', '0', '100k', '200k', '300k')) +
  labs(x = "Age band", y = "Population", fill = "Sex", 
       title = "Population in NSW by Age and Sex") +
  coord_flip() +
  theme_bw() +
  theme(plot.title = element_text(hjust = .5, face = "bold", size = 14),
        text = element_text(family = "Avenir", size = 12)) +
  scale_fill_manual(values = c("#1F988B", "#37598C"))

Figure 1: Population in NSW by Age and Sex

Population Density across NSW

erp_by_sa2 <- erp %>% group_by(sa2_code) %>% summarise(pop = sum(value))
total_pop <- sum(erp_by_sa2$pop)
erp_by_sa2 <- erp_by_sa2 %>% mutate(perc = pop / total_pop)

sa2_shape <- st_read("../../Raw Data/Data Files/ABS/SA2_Shapefile/SA2_2016_AUST.shp", quiet = TRUE)
combined_df <- merge(erp_by_sa2, sa2_shape, by.x = "sa2_code", by.y = "SA2_MAIN16")

combined_df %>% 
  filter(GCC_NAME16 == "Greater Sydney") %>% 
  ggplot() +
  geom_sf(mapping = aes(fill = pop)) +
  scale_fill_viridis("Population") +
  theme_bw()

Figure 2: Population Density across Greater Sydney

combined_df %>% 
  filter(SA2_NAME16 != "Lord Howe Island") %>% 
  ggplot() +
  geom_sf(mapping = aes(fill = pop)) +
  scale_fill_viridis("Population") +
  theme_bw()

Figure 3: Population Density across NSW

Household Composition

In the box plot, half of the SA2 regions have:

• around 3,000 to 7,000 population from 1-2 household size
• about 2,000 to 5,000 population from 3-5 household size, and
• around 150 to 500 from household size of over 5 people.

employed_household <- household %>% 
  left_join(employed, by = "sa2_code") %>% 
  na.omit() %>% 
  filter(hhold_fam_one + hhold_fam_multi + hhold_non_fam > 500)

employed_household %>% 
  gather(key = household_size_range, 
         value = household_size_value,
         c(hhold_size_1_2:hhold_size_over_5)) %>% 
  ggplot(mapping = aes(x = household_size_range, y = household_size_value)) +
    geom_boxplot() +
    scale_x_discrete(labels = c("1-2 people", "3-5 people", "Over 5 people")) +
    labs(x = "House Hold Size", y = "Population",
         title = "Distribution of House Hold Sizes Across SA2 Regions") +
    theme(plot.title = element_text(hjust = .5, face = "bold", size = 16),
          axis.ticks = element_blank(),
          text = element_text(family = "Avenir", size = 12))

Figure 4: Distribution of HouseHold Sizes Across SA2 Regions

There seems to be a little correlation between the unemployment rate and the percentage of over 5 people in house.

ggplot(data = employed_household, mapping = aes(x = perc_hhold_size_over_5, y = perc_unemployed)) +
  geom_point() +
  geom_smooth(se = FALSE, method = "loess", formula = "y ~ x") +
  labs(x = "Percentage of over 5 People in House", y = "Unemployment Percentage",
       title = "Unemployment Percentage vs Percentage of over 5 People in House") +
  theme_bw() +
  theme(plot.title = element_text(hjust = .5, face = "bold", size = 14),
        text = element_text(family = "Avenir", size = 12))

Figure 5: Unemployment Percentage vs Percentage of over 5 People in House